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Efficient Online Changepoint Detection using Functional Pruning CUSUM Statistics
Fast Online Changepoint Detection via Functional Pruning CUSUM Statistics Gaetano Romano, Idris A. Eckley, Paul Fearnhead, Guillem Rigaill; 24(81):1−36, 2023. Abstract The ability to process high-frequency observations with limited computational resources is crucial...
Derivative Calculation of High Energy Physics Programs Incorporating Discrete and Branching Randomness
The content can be rewritten as follows: arXivLabs is a platform that enables collaborators to create and share innovative features for arXiv directly on our website. Both individuals and organizations that collaborate with arXivLabs have embraced and embraced our...
Recursive Testing with Distance Assistance
DART: Distance Assisted Recursive Testing Xuechan Li, Anthony D. Sung, Jichun Xie; 24(169):1−41, 2023. Abstract In the field of modern data science, multiple testing is a widely used tool. In certain cases, the hypotheses are organized within a space where the...
Early Exiting for Efficient Deep Noise Suppression
arXivLabs is a platform designed for collaborative development and sharing of new features on the arXiv website. Both individuals and organizations that engage with arXivLabs are aligned with our core principles of openness, community, excellence, and user data...
Bayes-Newton Techniques for Approximating Bayesian Inference with Guaranteed Positive Semi-Definite (PSD) Properties
Bayes-Newton Methods for Approximate Bayesian Inference with Positive Semi-Definite Guarantees Authors: William J. Wilkinson, Simo Särkkä, Arno Solin; Volume 24, Issue 83, Pages 1-50, 2023. Abstract In this study, we propose a framework that formulates natural...
One-Bit Compressive Sensing for Communication-Efficient Decentralized Federated Learning
The popularity of decentralized federated learning (DFL) has increased due to its practicality in various applications. However, training a shared model among a large number of nodes in DFL is more challenging compared to the centralized version. This is because there...
Transforming Small Transformers to Compute Universal Metric Embeddings
Universal Metric Embeddings with Small Transformers Anastasis Kratsios, Valentin Debarnot, Ivan Dokmanić; 24(170):1−48, 2023. Abstract This study focuses on representing data from an arbitrary metric space $\mathcal{X}$ in the space of univariate Gaussian mixtures...
What insights can quantum convolutional neural networks provide us?
[Submitted on 31 Aug 2023] Download a PDF of the paper titled "What can we learn from quantum convolutional neural networks?" by Chukwudubem Umeano and 3 other authors Download PDF Abstract: This paper analyzes quantum convolutional neural networks (QCNNs) and...
Concurrent Reduction of Bias-Variance and Accelerated Convergence in Supercanonical Setting
Doubly Robust Stein-Kernelized Monte Carlo Estimator: Simultaneous Bias-Variance Reduction and Supercanonical Convergence Authors: Henry Lam, Haofeng Zhang; Journal of Machine Learning Research, 24(85):1−58, 2023. Abstract The standard Monte Carlo computation is...
An Autoencoder-based Approach to Monitor Data Quality in the CMS Electromagnetic Calorimeter
arXivLabs is a platform where collaborators can develop and share new features for arXiv directly on our website. Both individuals and organizations that collaborate with arXivLabs share our values of openness, community, excellence, and user data privacy. We are...
Diagonal Linear Networks: Unleashing Incremental Learning Potential
Incremental Learning in Diagonal Linear Networks Raphaël Berthier; 24(171):1−26, 2023. Abstract This paper discusses the trajectory of the gradient flow in Diagonal Linear Networks (DLNs) when initialized with small values. DLNs are simplified artificial neural...
Generating Satellite Images Generated Conditioned on Maps
[Submitted on 31 Aug 2023] Download a PDF of the paper titled Generate Your Own Scotland: Satellite Image Generation Conditioned on Maps, by Miguel Espinosa and 1 other authors Download PDF Abstract: Despite recent advancements in image generation, diffusion models...
Theory and Computation of Gaussian Processes Accounting for Errors in Variables
Gaussian Processes with Errors in Variables: Theory and Computation Authors: Shuang Zhou, Debdeep Pati, Tianying Wang, Yun Yang, Raymond J. Carroll; Volume 24, Issue 87, Pages 1-53, 2023. Abstract Covariate measurement error in nonparametric regression is a common...
Collaborative Experts: A Step towards Enhancing Graph Classification with Long-Tailed Recognition (arXiv:2308.16609v1 [cs.LG])
Graph classification has achieved remarkable results by learning graph-level representations for effective class assignments. However, most existing methods fail to optimize representation learning and classifier training jointly, especially for long-tailed graph data...
Exploring Variational Inequality Algorithms: Breaking Free from the Golden Ratio
Beyond the Golden Ratio for Variational Inequality Algorithms Ahmet Alacaoglu, Axel Böhm, Yura Malitsky; 24(172):1−33, 2023. Abstract This study aims to enhance the understanding of the golden ratio algorithm, which is used to solve monotone variational inequalities...
Learning How Urban Form Shapes Sustainable Mobility Across Continents: A Causal Discovery Approach
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Using Learning Maps to Study Function Spaces and Solve PDEs
Neural Operator: Learning Maps Between Function Spaces With Applications to PDEs Nikola Kovachki, Zongyi Li, Burigede Liu, Kamyar Azizzadenesheli, Kaushik Bhattacharya, Andrew Stuart, Anima Anandkumar; 24(89):1−97, 2023. Abstract The traditional development of neural...
Optimizing Patch Size in Vision Transformers for Tumor Segmentation
[Submitted on 31 Aug 2023] Download a PDF of the paper titled "Towards Optimal Patch Size in Vision Transformers for Tumor Segmentation" by Ramtin Mojtahedi and 3 other authors: Download PDF Abstract: Detection of tumors in metastatic colorectal cancer (mCRC) is...
Probabilistic Top List Predictions: Assessing their Elicitability
From Classification Accuracy to Proper Scoring Rules: Elicitability of Probabilistic Top List Predictions Johannes Resin; 24(173):1−21, 2023. Abstract In the field of forecasting, the importance of probabilistic assessments has been widely acknowledged to account for...
Semi-supervised Pre-training for Conversational Text-to-Speech Synthesis: Advancing Spontaneous Style Modeling
arXivLabs is a platform that enables collaborators to create and share innovative features on our website. We have garnered support from both individuals and organizations who align with our principles of transparency, collaboration, quality, and user privacy. At...
Non-Asymptotic Confidence Bounds for Recursive Quantile Estimation
Non-Asymptotic Confidence Bounds for Recursive Quantile Estimation Likai Chen, Georg Keilbar, Wei Biao Wu; 24(91):1−25, 2023. Abstract This study explores the use of the stochastic gradient descent (SGD) algorithm with Polyak-Ruppert averaging for recursive estimation...
A Multinational Retrospective Cohort SOPHIA Study: Creating and Validating an Explainable Machine Learning Calculator to Predict 5-Year Weight Changes Following Bariatric Surgery (arXiv:2308.16585v1 [cs.LG])
Background: The weight loss outcomes following bariatric surgery can vary significantly among individuals, making it difficult to predict the extent of weight loss before the operation. In this study, we aimed to develop a machine learning model that could provide...
Ensuring Posterior Consistency in Bayesian Relevance Vector Machines
Posterior Consistency for Bayesian Relevance Vector Machines Xiao Fang, Malay Ghosh; 24(174):1−17, 2023. Abstract The problem of statistical modeling and inference with sample sizes substantially smaller than the number of available covariates poses challenges. In a...
Curriculum-Learned Masked Autoencoders (CL-MAE): Enhancing Autoencoders through Curriculum Learning
[Submitted on 31 Aug 2023] Download a PDF of the paper titled CL-MAE: Curriculum-Learned Masked Autoencoders, by Neelu Madan and 4 other authors Download PDF Abstract: Masked image modeling has been proven effective in generating robust representations for multiple...
Theoretical Ideality and Practical Enhancements
Theoretical Optimality and Practical Improvements of Decentralized Learning Authors: Yucheng Lu, Christopher De Sa; Published in 2023, Volume 24, Issue 93, Pages 1-62. Abstract Scaling up parallel machine learning systems through decentralization has shown great...
A Comprehensive MViTv2 Based Approach for Document Layout Analysis on BaDLAD Dataset
In the fast-paced digital age, the analysis of document layouts is crucial for automated information extraction and interpretation. Our research focuses on training the MViTv2 transformer model architecture with cascaded mask R-CNN on the BaDLAD dataset. This allows...
Near-Optimal Sample Complexity in Zero-Sum Markov Games using Model-Based Multi-Agent RL
Model-Based Multi-Agent Reinforcement Learning in Zero-Sum Markov Games with Near-Optimal Sample Complexity Kaiqing Zhang, Sham M. Kakade, Tamer Basar, Lin F. Yang; 24(175):1−53, 2023. Abstract Model-based reinforcement learning (RL) is a fundamental approach in RL...
Mondeo: A Multistage Approach for Detecting Botnets
Mobile devices have become the most widely used technology, but they are also vulnerable to botnet-related malware. One example is FluBot, a botnet malware that specifically targets mobile devices. FluBot uses Domain Generation Algorithms (DGA) to communicate with its...
Statistical Inference for Binary Matrix with Noise and Incompleteness
Statistical Inference for Noisy Incomplete Binary Matrix Authors: Yunxiao Chen, Chengcheng Li, Jing Ouyang, Gongjun Xu; Volume 24, Issue 95, Pages 1-66, 2023. Abstract This paper focuses on statistical inference for noisy incomplete binary (or 1-bit) matrices. While...
Predicting Emergency Department Crowding using Advanced Machine Learning Models and Multivariable Input
The content can be rewritten as: arXivLabs is a platform where collaborators can create and share new features for arXiv directly on our website. Both individuals and organizations that collaborate with arXivLabs have embraced and adopted our values of openness,...
Assessing the Validity of Instruments through the Principle of Independent Mechanisms
Evaluating Instrument Validity using the Principle of Independent Mechanisms Patrick F. Burauel; 24(176):1−56, 2023. Abstract The validity of instrumental variables for estimating causal effects is often controversial and typically justified through narratives....
Scalable Alignment for Multi-View Clustering with Incomplete Data
arXivLabs is a platform that enables collaborators to create and share new features for arXiv directly on our website. Both individuals and organizations that collaborate with arXivLabs share our core values of openness, community, excellence, and user data privacy....
Estimating Maximum Likelihood for Autoregressive Graph Generative Models
Fitting Autoregressive Graph Generative Models through Maximum Likelihood Estimation Xu Han, Xiaohui Chen, Francisco J. R. Ruiz, Li-Ping Liu; 24(97):1−30, 2023. Abstract The objective of this study is to address the problem of fitting autoregressive graph generative...
The Interplay of Differential Games, Optimal Control, and Energy-based Models in Multi-Agent Interactions
[Submitted on 31 Aug 2023] Click here to download a PDF of the paper titled "On a Connection between Differential Games, Optimal Control, and Energy-based Models for Multi-Agent Interactions" by Christopher Diehl, Tobias Klosek, Martin Krüger, Nils Murzyn, and Torsten...
Evaluating Algorithm Portfolios Effectively through Item Response Theory
Evaluation of Algorithm Portfolios using Item Response Theory Authors: Sevvandi Kandanaarachchi, Kate Smith-Miles; Published in Journal of Machine Learning Research, Volume 24, Issue 177, 2023. Abstract Item Response Theory (IRT) is a method used in Educational...
Generative Models Enhanced with Neuro-Symbolic Constraints for Conditioning Score Estimation (arXiv:2308.16534v1 [cs.LG])
Score-based and diffusion models have become popular in generating both conditional and unconditional content. However, conditional generation typically requires training a conditional model or utilizing classifier guidance, even when a classifier for uncorrupted data...
Examining the Efficiency of Neural Network Pruning with Sparsity Perspective
Connectivity Matters: Neural Network Pruning Through the Lens of Effective Sparsity Artem Vysogorets, Julia Kempe; 24(99):1−23, 2023. Abstract Neural network pruning is an area of research that has gained significant interest, particularly in high sparsity regimes. In...
A Self-Adaptive Few-Shot 6D Pose Estimator for Novel and Occluded Objects: SA6D (arXiv:2308.16528v1 [cs.CV])
In order to enable effective manipulation of objects by robots in real-world settings, accurate estimation of their 6D pose is crucial. However, many current approaches struggle to make accurate predictions when faced with new instances of objects and heavy...
Cooperative Multi-agent Reinforcement Learning: A Flexible and Fully-Decentralized Approximate Actor-Critic Approach
F2A2: Flexible Fully-decentralized Approximate Actor-critic for Cooperative Multi-agent Reinforcement Learning Wenhao Li, Bo Jin, Xiangfeng Wang, Junchi Yan, Hongyuan Zha; 24(178):1−75, 2023. Abstract Traditional centralized multi-agent reinforcement learning (MARL)...
Pooling in Graph Neural Networks using Curvature (arXiv:2308.16516v1 [cs.LG])
The limitations of graph neural networks (GNNs) are often caused by over-squashing and over-smoothing. Over-smoothing erases node differences, while over-squashing hinders information propagation over long distances. These issues stem from the graph structure itself....
Inference for Matern Covariance Gaussian Processes on Compact Riemannian Manifolds
Inference for Gaussian Processes with Matern Covariogram on Compact Riemannian Manifolds Authors: Didong Li, Wenpin Tang, Sudipto Banerjee; Volume 24(101):1−26, 2023. Abstract Gaussian processes are widely used as versatile modeling and predictive tools in spatial...
Teaching Science and Testing Students through In-class Data Analysis Replications
The field of science is currently facing a crisis in terms of reproducibility. One potential solution that has been proposed is the incorporation of data analysis replications into classrooms. However, the feasibility of this approach and what stakeholders can expect...
Deblending Crowded Starfields Using Variational Inference
Variational Inference for Deblending Crowded Starfields Authors: Runjing Liu, Jon D. McAuliffe, Jeffrey Regier; Published in 2023, Volume 24(179):1−36 Abstract In astronomical survey images, it is common for stars and galaxies to visually overlap. Deblending refers to...
The title “Latent Painter” could be rewritten as “The Latent Painting Algorithm” (arXiv:2308.16490v1 [cs.CV])
The introduction of latent diffusers has brought about a revolution in generative AI and has served as a source of inspiration for creative art. By denoising the latent, the predicted original image progressively brings to life the formation process. However, the...
Training with Sparse Lipschitz Continuous Loss Functions and a Weighted Group L0-norm Constraint
Sparse Training with Lipschitz Continuous Loss Functions and a Weighted Group L0-norm Constraint Michael R. Metel; 24(103):1−44, 2023. Abstract This research paper focuses on the application of structured sparsity in deep neural network training. The study explores...
Meta-Learning: Enhancing Point Cloud Upsampling through Test-Time Adaptation (arXiv:2308.16484v1 [cs.CV])
The content can be rewritten as follows: Many affordable 3D scanners have a drawback of producing point clouds that are sparse and non-uniform. This can negatively affect the performance of robotic systems in downstream applications. Although existing point cloud...
Distributionally Robust Optimal Dropout Training
Dropout Training: Distributional Robustness and Optimal Solution José Blanchet, Yang Kang, José Luis Montiel Olea, Viet Anh Nguyen, Xuhui Zhang; 24(180):1−60, 2023. Abstract This study demonstrates that dropout training in generalized linear models represents the...
Enhancing Automatic Echocardiographic Analysis with Integrated Out-of-Distribution Detection in Echocardiographic View Classification
arXivLabs is a platform where collaborators can create and share new features for arXiv directly on our website. Both individuals and organizations that collaborate with arXivLabs share our values of openness, community, excellence, and user data privacy. We are...
Integrating Knowledge Hypergraph Embedding with Relational Algebra
Knowledge Hypergraph Embedding Meets Relational Algebra Authors: Bahare Fatemi, Perouz Taslakian, David Vazquez, David Poole; Volume 24, Issue 105, Pages 1-34, 2023. Abstract Relational databases have been successful in data storage and rely on query languages for...
Test-Time Adaptation for Point Cloud Registration Using Multitask Meta-Auxiliary Learning (arXiv:2308.16481v1 [cs.CV]) – Point-TTA
Introducing Point-TTA, a new framework for point cloud registration (PCR) that enhances the performance and generalization of registration models. Despite the impressive progress made by learning-based methods, adapting to unknown testing environments remains a...
Factor Graph Neural Networks: A Novel Approach to Graph-based Learning
Factor Graph Neural Networks Zhen Zhang, Mohammed Haroon Dupty, Fan Wu, Javen Qinfeng Shi, Wee Sun Lee; 24(181):1−54, 2023. Abstract In recent years, Graph Neural Networks (GNNs) have gained significant popularity and have achieved remarkable success in various...
Adapting Policies for Implicit Multitask Reinforcement Learning Challenges
The study focuses on dynamic motion generation tasks, such as contact and collisions, where small changes in policy parameters can have a significant impact on the outcomes. For instance, in soccer, even a slight variation in the hitting position, applied force, or...
Bounds on Risk for Positive-Unlabeled Learning Assuming Random Selection
Risk Bounds for Positive-Unlabeled Learning Under the Selected At Random Assumption Olivier Coudray, Christine Keribin, Pascal Massart, Patrick Pamphile; 24(107):1−31, 2023. Abstract Positive-Unlabeled learning (PU learning) is a variant of semi-supervised binary...
Rephrased title: A Graph Neural Network with Domain Adaptation for Message Passing (arXiv:2308.16470v1 [cs.LG])
Recently, there has been a growing interest in Cross-network node classification (CNNC) which involves classifying nodes in a target network with limited labels by leveraging knowledge from a source network with abundant labels. In order to tackle this problem, we...
Weaker Assumptions Needed for Naive Regression Compared to Factor Models in Adjusting for Multiple Cause Confounding
Naive Regression vs. Factor Models: Adjusting for Multiple Cause Confounding Authors: Justin Grimmer, Dean Knox, Brandon Stewart; 24(182):1−70, 2023. Abstract Factor models are commonly used in various fields, such as genetics, networks, medicine, and politics, to...
Normalizing Flows for Computing Excited States of Molecules (arXiv:2308.16468v1 [physics.chem-ph])
We introduce a novel approach for computing both ground and excited states of quantum systems. Our method utilizes a nonlinear variational framework and involves approximating wavefunctions using a linear combination of basis functions. These basis functions are...
Dimensionless machine learning: Enforcing precise units equivariance
Dimensionless Machine Learning: Enforcing Exact Units Equivariance Soledad Villar, Weichi Yao, David W. Hogg, Ben Blum-Smith, Bianca Dumitrascu; 24(109):1−32, 2023. Abstract Units equivariance (or units covariance) arises as an exact symmetry when the relationships...
Contextual Pragmatic Knowledge as a Benchmark for Bioinformatics Code Generation
arXivLabs provides a platform for collaborators to create and share new features on the arXiv website. Both individuals and organizations that collaborate with arXivLabs share our core values of openness, community, excellence, and user data privacy. We are dedicated...
The Relationship between Width and Depth of Neural Networks: A Quasi-Equivalence
Quasi-Equivalence between Width and Depth of Neural Networks Fenglei Fan, Rongjie Lai, Ge Wang; 24(183):1−22, 2023. Abstract The classic studies have shown that wide networks have the ability to universally approximate, while recent advancements in deep learning have...
A revised version of the title: A Study on Least Squares Maximum and Weighted Generalization-Memorization Machines (arXiv:2308.16456v1 [stat.ML])
In this paper, we present a novel approach to memory in the least squares support vector machine (LSSVM). Our method introduces a memory influence mechanism that allows for accurate partitioning of the training set without overfitting, while maintaining the equation...
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